The purpose of this project is to explore the use of queueing network models in developing a decision support tool that assists mental health service planners to formulate and evaluate residential capacity decisions. Specifically, this planning model is designed to address the problem of clients remaining in more restrictive care settings than is clinically required due to lack of alternative residential options. Recently, a vast amount of progress has been made in the development of queueing models that deal with congestion in a network with finite capacity in diverse scientific fields such as Operations Research, Computer Science, and Communication Engineering. The recent advancement in this modeling technology opens up an opportunity for mental health service researchers to apply this technology to the mental health services system. This project will apply a queueing network approach to model the client flow throughout the institutional/long-term hospital and residential service system for individuals with serious mental illness (SMI). The increase or decrease in the queues (i.e., unnecessary stays) will be calculated in relation to changes in current bed capacity. Simulation analysis will be carried out to test the robustness of the results of the analytical model. Input parameters to specify the queueing network model and simulation algorithms will be derived from the hospital and residential service utilization and referral data from the Philadelphia mental health system. [unreadable] Future plans are to improve the applicability of this model to other mental health systems and to improve the accuracy of predictions. The planning model will facilitate the development of service configurations that provide a better match between level of care and needs of clients through reducing the extent of unnecessary stays than those based on current decision making practice. [unreadable] [unreadable]